The Impact of Service Automation on Customer Satisfaction ...zantworldpress.com/wp-content/uploads/2015/05/504-Victor-Ong.pdf · [email protected] . Proceedings of 4th

Proceedings of 4th Global Business and Finance Research Conference

25 - 27 May 2015, Marriott Hotel, Melbourne, Australia, ISBN: 978-1-922069-76-4

The Impact of Service Automation on Customer Satisfaction and Customer Retention: An Empirical

Study of Malaysian Rail Transportation

Victor Ong, Ng Mei Yee, Giam Jing Hui, Nurdina Kasim and Izya Hizza

Purpose To examine how the impact of automated service attributes on the service encounter satisfaction and customer retention and to provide fundamental insights for rail transportation in future service planning decisions. Methodology Quantitative method was performed. The primary data was collected by distributing the self-administrated questionnaires and was coded, captured and analyzed in SPSS18.0 software. Descriptive analysis and inferential analysis were used to assess the structural model. Findings The results show that the attributes of convenience and customization contribute towards the service encounter satisfaction with automated services, which then lead to customer retention. The findings reinforced that the convenience of automated service as a major determinant of technological acceptance as well as customer adoption. Practical implication The Malaysia government can develop better automated service system in rail transportations to attain high efficiency and excellent customer experience in service delivery based on the analysis result. Originality/value This study fulfills an identified information need in rail transportation industry in Malaysia and provide the government a practical insight in developing automated service system in the future especially towards the MRT project. Keywords: Service encounter satisfaction, Customer retention, Automated service, Reliability, Convenience, Customization.

Introduction With the rapid evolution of society and the advent of technology, the demands for public transportation is an important and challenging issue in a country that is fast progressing (Bardi et al., 2011). Public transportation provides people with mobility and access to employment, institutional resources, medical care, and recreational opportunities in societies across a country. The integration of public transportation channels into wider economic and land use planning can always help a community enhance business opportunities, and more importantly, from a citizenry point of view, it creates a sense of community through transit-oriented development (Aziz and Amin, 2012). However, the Malaysian economy is developing so fast that most of the people can afford to own means of transport and hence the vehicle population has also boomed. This is further worsened by the lack of a comprehensive public transport infrastructure, not to mention inadequate parking spaces. Air pollution and other environmental hazards are also yet another cause for _____________________________________________________ Sunway University Business School, Sunway University, Malaysia. Email: [email protected], [email protected], [email protected], [email protected], [email protected]

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concern. Yet, many people are avoiding public transport such as buses or trains due to long queues and long waiting periods. Of course, this adds to the crowding at such places and serves to encourage transport ownership as a result. As part of any orderly development in a city that is constantly growing, the authorities must confront the issues relating to public transport and address the problems that accompany their use. The transport networks must also prepare for the future in order that they can cater to a growing urban population as well as increasing expectations of an urbanized society. In recognition of this, the government in the mid-1990s embarked on a programme of massive investments in public transport – and in railway infrastructure, it has completed three major intra-city rail systems in the Klang Valley (i.e. the metropolitan areas of Kuala Lumpur and the surrounding satellite towns). These are the Keretapi Tanah Melayu (KTM), Light Rail Transit (LRT) and Monorail. In recent years, the Malaysian government has proposed to spend US$50 billion to further develop the rail networks over the next seven years – the development of the Mass Rapid Transit (MRT) – launched in 2011 – as well as to extend the current rail connectivity to embrace a bigger geographical area and in the process, integrate the existing rail networks.

Research Problem Definition For developing countries looking at sustainable development, urbanization is one of those challenges planners have to deal with. Particularly in the area of labor mobility and the movement of people from one place to another without suffocating our busy roads, thereby contributing to a worsening environment and health-related issues. The level of urbanization in Malaysia is 38% and growing – therefore, this issue induces constant developments in the fast-changing transportation industry. In this era of technology advances, there are evolving transportation systems that are capable of satisfying the demanding individual‟s travel challenges at both intra-city and inter-city levels. Since many researches have shown that the traditional service encounters which involve high levels of manpower have led to customer dissatisfaction and complaints – the authorities have implemented the automated service system in rail transportation to enhance service encounters thereby allowing customers to perform the necessary services with less or even without human interaction. Automated service system is the technique use in industrial control that could execute automatically through electronic control system. The examples of automated services in rail transportation are the automated fare collection systems, platforms arrival systems, ticket vending machines, security cameras and other specific automated systems. The implementation of automated system in rail service encounters could lead to the improvements in service efficiencies and quality of management information (Baragwanath, 1998). Hence, this proposed study is to gain valuable information to assist the authorities in enhancing rail transportation in Malaysia by using the automated



service system to achieve high efficiency and deliver superior services to the travelling public. Moreover, this study examines how customers respond to the attributes of reliability, convenience and customization towards the adoption of automated service systems. When we ascertain the results, the authorities can evaluate the effectiveness of the system and predict the response of customers towards this self-service technology, i.e. SST (Mokhtarian, n.d).

Research Objectives This study is to examine how the attributes of automation service (reliability, convenience and customization) impact on service encounters resulting in customer satisfaction and customer retention. The reason to conduct this study is to provide further insights in the provision of rail transportation in respect to future planning decisions in Malaysia. The development of the Mass Railway Transit (MRT) project through service automation which is currently ongoing is a case in point. Our investigation via this proposed study allows us to uncover helpful information that can allow the government and related parties to appreciate the main indicators of customer satisfaction with regard to service automation. And this study helps to determine to what extent customers‟ satisfaction with automated service encounters in the country does affect customer retention as an additional outcome.

Literature Review

The Attributes of Service Automation The importance of service attributes was determined in the previous researches, which particularly stressed on the relationship between overall satisfaction and attributes (Mittal et al., 1998). Cronin and Taylor (1992) suggested that particular attributes are varied among different industries depending on its importance to that industry. The service attributes may vary across the form of service delivery either self-service technology or personal service (Beatson, Lee and Coote, 2007). The advancement of technology has brought prominent influences to the development of service delivery alternatives and impactful outcome on service marketing (Dabholkar and Bagozzi, 2002). Today, the service automation within service industries is having a substantial impact on traditional methods of business and the strategies undertaken by organizations. The automated service attributes have been identified in previous research which includes reliability of the technology, convenience of the technology and customisation of the technology (Beatson et al., 2007; Meuter et al., 2005; Walker et al., 2002.) Service Automation Automated service presents the organisations various potential opportunities in developing service design strategies and new service development (Henderson et al, 2003). Automated service originally defined as „interactive, content-focused and web-based customer service which demonstrated by the customers and synthesized with related organizational customer support process and technologies with the objective of enhancing the customer and



service provider relationship‟ (Ruyteret al., 2001). Surjadjajaet al.(2003) explained automated service is any online-based service served via the internet whereby the interaction between customer and the organisation is limited to the information and communication technology (ICT) itself. However, the consideration of other essential automated service dimensions such as telephone services and automated service delivery outlets have not been mentioned in these explanations. Buckley (2003) has further extended the definitions of automated service as the electronic stipulation of a service to customers. This definition is demonstrated in a holistically manner since it involves the consideration and assessment of other service delivery methods beyond services through the internet. Reliability Reliability is a key attribute of excellent customer service since it determines the repeated use of certain services (State Library of Victoria, 2009). Reliability defines as the ability of a system to keep operating over time (Clements et al., 2002). Parasuraman et al. (1988) also explained that reliability is the capability to achieve the promised service consistently and accurately. Customers will refuse to exercise automated service options on their next visit if a poor first experience is encountered with the technologies. A consistent and dependable service will always increase a customer‟s satisfaction and confidence with the rail transportation service. Therefore, the dissatisfaction on the services offered owing to poor quality in term of performance efficiency and information accurateness always cause to loss of customer. Reliability encourages customer trust toward certain services which can be verified through the functionality and speed of technology, streamlining and maintaining procedures and maintaining standards of service delivery (State Library of Victoria, 2009). Convenience The practice of convenience is another key factor that forms customer perceptions of service quality (Amorim et al., 2012a). The improvements in process convenience demonstrate a powerful impact on customer return intentions to service alternative, especially the automated service driven by technology, which also known as self-service technology option (Amorim et al., 2012b). This emphasizes the implication of perceived convenience in developing customers' motivation to choose service alternatives must involve high degree of customer participation such as waiting less time in line. In order to attain outstanding service quality, service differentiation can be carried out based on the operational characteristics of each delivery alternative. Customization Service customization has become increasingly popular in the service industry, which means to assure as many needs as possible for each individual customer (Kara and Kaynak, 1997). Anderson et al. (1997) defines customization as the degree to which the service provider‟s offering is tailored to meet heterogeneous customers‟ needs. Kotler (1989) and Pine (1993) explained customization as the response to the irregular nature of customer



demand for greater selection, extra attributes, and better value in services. Driven by the expected benefits, customization is always the foundation of comprehensive customer relationship management (Freeland, 2003; Lemon et al., 2002). Undoubtedly, the revolutions in computer system and the wide-spread of the Internet have offered potential opportunities to marketers to customize offerings to fit the demand of diverse populations. Many researchers predicted that corporate investment in customized technologies will expand remarkably in the future due to the increase of market demand (Gardyn, 2001; Kim et al., 2001; Rust and Lemon, 2001). This trend stays on track with companies‟ strong desires for latest nature information and extent of demand for service customization (Liechty et al., 2001).

Service Encounter Satisfaction The customer is considered the critical aspect for service providers to achieve service innovation and to ensure sustainable business value and profits in the process of conveying service experience (Hsieh and Yuan, 2010). Researchers agreed that consumer satisfaction resulted from a subjective relationship between expected and perceived attribute levels. The term customer satisfaction at the point of delivery as used herein is consistent with the definition of service encounter satisfaction, i.e. customer satisfaction with a distinct service encounter normally reflects the feelings of customer about a specific interaction or moment of truth. The expectancy disconfirmation model proposed by Oliver (1989) has presented a conceptual and empirical support in signifying the customer satisfaction which represents how reasonably the services provided match customer expectations. A positive disconfirmation occurs when customer perception exceeds customer expectation, and thus the customer is satisfied with the service. In contrast, dissatisfaction responds to negative disconfirmation which indicates the service provider failed to identify the customer‟s expected quality on certain service. However, Woodruff et al.(1983a) identified that consumers have a “zone of indifference” in evaluations which demonstrating performance may not fall under the expectation models with certain aspects of the encounter. Zone of indifference therefore referred to the range of disconfirmation which presumably leads to a neutral state. Consequently, customer satisfaction is “a psychological form ensuing when the emotion surrounding disconfirmed expectations is accompanied with the consumer‟s preceding opinions about the consumption experience” (Oliver, 1981). The disconfirmation model described in Figure 1 is based on this Woodruff et al. (1983b) notion as demonstrated by Hill (1986).



Figure 1: The Disconfirmation Model

Expectations principally identified as the predictions about certain events that likely to occur during the impending exchange, which also apply as a reference against which performance can be compared and assessed disconfirmation. It often conceptualized as combination of customer wants and customer beliefs about what the service provider is capable of providing (Zeithamlet al., 1993a). Parasuraman et al. (1991) proposed that customer expectations comprise two levels: desired and adequate. Desired expectation implies the level of service a customer desires to receive, in short, it is the level of service performance that the customer expects. Adequate expectation explains a lower level of expectation which considers as the customer‟s acceptable level of performance (Zeithaml et al., 1993b).

Customer Retention Retention can be generally defined as “to continue to do business or exchange with the particular firm continuous basis” (Zineldin, 2000). Potter-Brotman (1994) stated that services does effect customer retention positively and influence service providers to enhance relationship with customer by creating satisfaction. Some researchers argued trust has a stronger emotion that provide a better prediction on retention instead of customer satisfaction (Hart and Johnson, 1999). Dawkins and Riechheld, 1990) cited by Ahmad and Butte (2001) had claimed that there are a 5% increase of retention will lead an increase of the value of customers, which is between 25% and 85% increase in many industries. Ahmad and Butte (2001) also stated that customer retention deserve better attention in a part of strategic marketing goals, rather than being seen as a result of “good” marketing management.

Gustafsson et al. (2005) has identified three important drivers of customer retention in their previous literature. Those attributes consist of the customer satisfaction, affective commitment and calculative commitment. Affective commitment is seen to be more emotional due to the high level of trust which established through personal involvement between the customer and the service providers (Gabarino and Johnson 1999; Morgan and Hunt 1994). Cumulative commitment is seen to be economic oriented because the customer only depends on the product or service benefits owing to the lack of alternative or price related factor (Anderson and Weitz, 1992; Dwyer et al.,



1978; Heide and John, 1992). Importantly, customer satisfaction demonstrates the direct effects to the service provider‟s market share and customer retention (Rust and Subramanian, 1992). However, Hansemark and Albinson (2004) critiqued that sometimes the customer not return to the firm may not necessarily because of the satisfaction. Customers retain in the company not because they are satisfied, but because they are lack of product alternatives (Erikson and Lofmarck Vaghult, 2000). Moreover, Venetis and Ghauri (2004) found out that there service quality directly an addition to customer retention, regardless for their desire to need to stay in the relationship. Hansemark and Albinson (2004) has found out that retention can be recognized by the satisfaction of customer towards the service, by developing good relationship with customers which create mutual confidence. The higher the level of satisfaction, the greater the retention of customers (Ranaweera and Prabhu, 2003; Anderson and Sullivan, 1993; Fornell, 1992).

Hypothesis Model

Figure 2 Hypothesis Model

A proposed hypothesis model of this research is developed by combining the theoretical framework of service encounter evaluation model (Oliver and Swan, 1989) and customer satisfaction evaluation model based on self-service technology (Beatson et al., 2007). This framework proposed the direct relationships exist from the attributes of automation in rail transportation to service encounter satisfaction. For example, it can be presumed that if consumers rate the performance of the various components of the service positively they are more likely to be satisfied overall with the complete service experience. The automated service attributes had been identified in previous researches (Dabholkar, 1996; Meuter et al., 2000; Walker et al., 2002) which includes the reliability of technology, the convenience of technology and the customisation of technology. Based on these major attributes, 3 hypotheses have been proposed to determine the customer satisfaction towards the automated service encounter in rail transportation:



Furthermore, a comprehensive investigation of consumer retention is developed in this framework based on the domain of automated service encounter. The importance of understanding automation impact on customer retention since the industry cannot survive without repeat usage (Beatson. A. et al., 2007b). By having automated technology services, organisations are reducing personal contact which is believed to have a positive influence on consumer retention (Buell et al., 2010). A previous study showed that high extend of customer satisfaction translated into customer retention is radically reduced by approximately 60% in cases where the customers were not as strongly satisfied (Chandrashekaran et al., 2007). Based on the overall empirical evidence, this study thus hypothesized:

Methodology Quantitative methods will be using for this study which usually employs to measure social reality as it produces a high accuracy level with the statistical data. The primary data was collected by distributing the survey questionnaires. Quantitative method on the other hand will be used to analyse customer satisfaction towards the automated services in rail transportation. The design of practical task involved in this research has been summarized in Table 1. The self-administered questionnaires were distributed through on-site distribution and Internet. In term of on-site survey, data was collected mainly from Sunway area, Mid Valley-The Gardens, KL Sentral, and Puchong Area, while the Internet survey data was collected through Facebook and e-mail. The respondents represented a good mix of university students as well as the workforce members from all age group.

RESEARCH SCOPE Automation in rail transportations

GEOGRAPHIC LOCATION

Klang Valley

SURVEY METHODOLOGY

Self- administered questionnaire

TYPE OF SAMPLING Convenience sampling

SAMPLE SIZE N=120

H1: Reliability of the technology in rail transportation has significant influences

on service encounter satisfaction. H2: Convenience of the technology in rail transportation has significant

influences on service encounter satisfaction. H3: Customization of the technology in rail transportation has significant

influences on service encounter satisfaction.

H4: The service encounters satisfaction lead to customer retention in rail transportation.



As mentioned in the hypothesis model, the major framework components that found in the research comprised the service encounter evaluation model (Oliver, 1980 and Swan, 1983) and customer satisfaction evaluation model based on self-service technology (Beatson et al 2007).The questionnaire is divided into 5 sections. Based on the respondents‟ experience, section A and section B gathers the information about the 3 major attributes of automated services in rail transportation as demonstrated in H1,H2 and H3. To carry out the proposed objectives of this study, Beatson‟s et al. (2011) study is adapted to measure the influence of each automated service attributes. Section C covered the information on the general opinion about the service encounter satisfaction with service automation in rail transportation, while section D covered the intention of customer to stay with the automated services. Lastly, the respondent‟s demographic profiles were asked in Section E to compare the result differences based on different characteristics. To analyze the collected data, method of descriptive analysis and inferential analysis were employed by using the SPSS 18.0 software. Descriptive analysis was used to describe the personal details. The general measures of percentage are used to analyze the data collected throughout the questionnaires. Inferential analysis is used to make prediction and inferences about a population from the sample analysis. The result of the analysis can generalize the using sample to the larger population that the samples represent. In order to test the issues of generalization, the test of significance is necessary to perform in order to show how likely the result is due to the chance (Creative Research System, 2012). The example of this methods included reliability test and multiple linear regression. Rail transportation was measured by quantitative data which is Likert scales to measure the factors. The Likert scale is a uni-dimensional and able to design into 5-7 choices. This study only designs into 5-point Likert scale as being categorized as “Strongly Agree, Agree, Neutral, Disagree and Strongly Disagree”.



Data Analysis

Descriptive Results

Demographic Variable

Details Frequency Percentage (%)

Gender Male 60 50

Female 59 49.2

Age Range 45 4 3.3

Marital Status Single 40 33.3

Married 79 65.8

Others 1 0.8

Employement Level

Employed 76 63.3

Student 40 33.3

Housewife 3 2.5

Others 1 0.8

Education Secondary 16 13.3

Diploma 26 21.7

Degree 47 39.2

Postgraduate 24 20.0

Others 7 5.8

Table 1 Demographic Profile of Respondents

The respondents, who are eligible to receive the questionnaire, were students and office workers in Malaysia, thus the sampling frame for the sample is completed trough convenience sampling from which the population is drawn. According to 120 total respondents from the satisfaction of using automated services in railway transportation in Malaysia were obtained, there are 60 female respondents and the rest are males. The respondents has a dominant average age of 18 -24 years old (53.3%), followed by 25-34 years old (28.3%) and least is 45 years old and above (0.3%). Based from the respondents, they have a dominance of (65.8%) of them are married followed by single (33.3%) and others (0.8%). The highest education level of the entire sample contributes to degree students (39.2%) and the least majority of respondents are professional terms of profession (5.8%). In addition, 63 % of the respondents are employed followed by 33 % are students. The minority of the



respondents under employment status are Housewives (2.5%) and others (0.8%).

Cronbach's Alpha for Each Variable Reliability

Reliability Statistics

Cronbach's Alpha Cronbach's Alpha Based on Standardized Items

N of Items

.913 .914 5

Convenience



N of Items

.880 .880 5

Customization



N of Items

.680 .680 2

Customer Satisfaction



N of Items

.815 .816 6

Customer Retention



N of Items

.825 .828 3

Based on the table above, Cronbach's alpha also indicated in this study. Cronbach's alpha is the measurement of the uni-dimensional of each construct should consider higher than 0.5 for acceptance level of reliability. There are five variables included in this study which consists of reliability, convenience, customization, customer satisfaction and customer retention.



The Cronbach's alpha value for reliability is 0.913, convenience is 0.880, customization is 0.680, customer satisfaction is 0.815, and customer retention is 0.825. Each variable reached the acceptance level which means all the variables are reliable to adapt in this study. The means for each variable were calculated to conduct correlation and linear regression analysis. The table below shows the Pearson‟s correlation coefficients for customer satisfaction with the other three variables, which are reliability, convenience and customization.

Correlations Customer

Satisfaction Reliability Convenience Customization

Pearson Correlation

Customer Satisfaction 1.000**

Reliability .484** 1.000

Convenience .580** .814** 1.000

Customization .618** .614** .692** 1.000

Sig. (1-tailed)

Customer Satisfaction . .000 .000 .000

Reliability .000 . .000 .000 Convenience .000 .000 . .000 Customization .000 .000 .000 .

N

Customer Satisfaction 120 120 120 120

Reliability 120 120 120 120

Convenience 120 120 120 120

Customization 120 120 120 120

**Correlation is significant at the 0.01 level (two-tailed)

Table 2 Result of correlation The relationship between the independent variable, reliability and the dependent variable, customer satisfaction has a slightly weak positive correlation of 0.484. Convenience and customer satisfaction has a partially positive relationship of 0.580. The correlation between customization and customer satisfaction however resulted in a slightly strong positive correlation, which is 0.618. R-square value was found to be 0.427, which means 42.7% of the variation of customer satisfaction, can be explained by reliability, convenience and customization. After determining the correlations, a standard regression analysis is done and the table below shows the unstandardized coefficients extracted from multiple linear regression analysis.

Coefficientsa Model Unstandardized

Coefficients Standardized Coefficients

t Sig. Correlations Collinearity Statistics

B Std. Error Beta Zero-order

Partial Part Tolerance VIF

1

(Constant) 1.170 .186 6.283 .000

Reliability -.023 .097 -.029 -.235 .815 .484 -.022 -.017

.332 3.013

Convenience .253 .107 .315 2.358 .020 .580 .214 .166 .278 3.603

Customization .347 .081 .418 4.259 .000 .618 .368 .299 .514 1.947

a. Dependent Variable: Customer Satisfaction



Table 3 Result of Coefficients

Table 3 demonstrated the result of coefficient from SPSS data. In linear regression, the model specification is that the dependent variable is a linear combination of the parameters. B represents Beta value in the Table of Correlation. The following equation is added the coefficient for the following multiple linear regression model:

=-0.023 + 0.253 + 0.347 The equation signifies that each unit increase in convenience or customization will leads to an increase in customer satisfaction given that all variables remain constant. However an increase in reliability will cause customer satisfaction to decrease if all variables remain constant. Based on the Coefficient Summary table, this study signified the result of hypothesis as below:

H1: Reliability of the technology in rail transportation has significant influences on service encounter satisfaction.

Decision rule As explained in the Table 3, the t-statistic for the reliability of automation services and customer satisfaction is -0.235 (p-value 0.05) and Beta is -0.029. Thus, there is sufficient proved that the respondents state convenience in using automation service in railway transportation is significance influence customer satisfaction. Since p-value is 2.358> 0.05, therefore H2 is accepted. It showed that it is a positive relationship between convenience and customer satisfaction.

The linear regression equation for the convenience of automation services and CUSTOMER SATISFACTION = 1.170 + 0.253 (CONVIENIENCE). This means that for every unit increase in attitude, the purchase intention will be affected and increase by 0.253.

http://en.wikipedia.org/wiki/Linear_combinationhttp://en.wikipedia.org/wiki/Linear_combination



H3: Customization of the technology in rail transportation has significant

influences on service encounter satisfaction. Decision Rule As explained in the Table 3, the t-statistic for the customization of automation services and customer satisfaction is 4.259 (p-value > 0.05) and Beta is 0.418. Thus, there is sufficient proved that the respondents state there is a convenience in using automation service in railway transportation is significance influence customer satisfaction. Since p-value is 4.259> 0.05, therefore H3 is accepted. It showed that it is a positive relationship between customization and customer satisfaction.

The linear regression equation for the convenience of automation services and CUSTOMER SATISFACTION = 1.170 + 0.347 (CUSTOMIZATION). This means that for every unit increase in attitude, the purchase intention will be affected and increase by 0.347.

Result Conclusion On the whole, customization (4.259)has the highest explanatory power as compared to the other predictor variables: reliability (-0.235) convenience (2.358). Furthermore, the coefficient summary table has also indicated that customization and convenience have positive relationship to customer satisfaction since the p-value of both factors is less than 0.05

Regression for Customer Satisfaction and Retention

Model Summaryb

Model R R Square Adjusted R Square Std. Error of the Estimate

1 .675a .456 .452 .61059

a. Predictors: (Constant), Customer Satisfaction

b. Dependent Variable: Customer Retention

Table 4 Model Summary



Multiple regression is an equation that is represented the best prediction of a dependent variable from several independent variables. This analysis is used when the independent variable are correlated with one another with the dependent variable (Coakes & Ong, 2011). As the second correlation after examining the three attributes, the relationship of customer satisfaction and retention will be tested. In this context the aim of using the multiple regressions is to test if the levels of consumer‟s satisfaction (independent variable) correlate with customer retention (dependent variable) in automated services in Malaysia railway transportation. This is because individuals have variability in their behavior and an action which is a challenge for researchers to concretely predict. Based from the results in Table 3 the correlation between independent and dependent variable explains 45 per cent of the variance (R Square) in customer satisfaction on automation service, which is somewhat highly significant.

ANOVAa

Model Sum of Squares df Mean Square F Sig.

1

Regression 36.897 1 36.897 98.967 .000b

Residual 43.992 118 .373

Total 80.889 119 a. Dependent Variable: Customer Retention b. Predictors: (Constant), Customer Satisfaction

Table 5 ANOVA

The ANOVA table (Table 4) has illustrated that the F-value is 98.967 and the p-value is less than 0.05, which indicated that the variable of the study is significant.

Coefficients

Coefficientsa

Model Unstandardized Coefficients

Standardized Coefficients

t Sig. Correlations Collinearity Statistics

B Std. Error Beta Zero-order

Partial Part Tolerance VIF

1

(Constant) .449 .230 1.952 .053 Customer Satisfaction

.797 .080 .675 9.948 .000 .675 .675 .675 1.000 1.000

a. Dependent Variable: Customer Retention

Table 6 Coefficient Table 5 show the coefficient results produced from the test. Based on the table, the following equation can be constructed: y = 0.449 + 0.797 x1 Table 5 also stated that the t-value for customer satisfaction is 9.948 which is (p



H4: There is a significant relationship in customer satisfaction in influencing customer retention towards automated services in railway transportation in Malaysia.

Discussion and Findings Reliability in the automated services can be separated into two parts which are functional and technical (Narteh, 2013). Functional aspects of reliability describe the ability of automated service to deliver as expected with no errors whereas on the other hand technical reliability is more towards the quality and the design of the service (Narteh, 2013). Automated service is more consistent and has low possibility of distortion in delivering service. Therefore railway transportation users prefer automated service, as it is less prone to make errors and mistakes in comparison to normal human interactions. The result of „reliability‟ in this study is consistent and similar to other previous studies, which indicates that there is relationship between reliability and customer satisfaction. Narteh (2013) stated in his study that reliability is a fundamental factor to determine the quality of automated services and it also serves as the strongest predictor to customer satisfaction. The results obtained for this research shows there is no relationship between convenience and customer satisfaction. However, convenience should not be neglected as it still plays a role to increase customer satisfaction. In the literature review, it is stated that customers are more willing to take part in activities that requires less involvement and effort such as queuing in line. This behaviour can be explained by using a study by Narteh (2013) where he describes convenience to be a major determinant of technological acceptance as well as customer adoption. The automated services should be user friendly in order to attract customer because it will provide ease of use while using the automated service. Location and the number of the service provided should also be taken into consideration as it must be located that right place to avoid congestion. The other variable that is included in this study is customization. Although the relationship between customization and customer satisfaction is insignificant, there is still some evidence that can support H1. Ahmad A (2011) uses a different term, i.e. „Personalization as it refers to the possibilities of personalized service through automated service channels. Railway transportation needs to customize automated services in order to be able to target certain user groups and hence gain competitive advantage (Ahmad, 2011). Based on our literature review, due to the criteria of customization that meet specific demands, this acts as an important factor in customer relationship management and consequently will then increase the customer satisfaction (Freeland, 2003; Lemon et al., 2002). However using fully automated service, it can be hard to provide value to users without any human interaction (Ahmad Al-Hawari, Ward and Newby, 2009). From the proposed framework, the researchers had assumed that there are direct relationships that occur in customer satisfaction of automated services in railway transportation for the second stage of the framework. The first stage includes how attributes of service automation influence customer satisfaction. The customer relationship can benefit from in-depth factors that influence



retention towards the automation service. The customer satisfaction and the relationship based from the marketing literature stated that there are generally a significant relationship of both retention and satisfaction. A research by Potter-Brotman (1994) clarified that service providers create customer retention by enhancing the relationship with consumers to create satisfaction. Based on the results, the attributes for retention are highly significant which relates to the previous literature that suggest three basic predictions of retention (overall customer satisfaction, affective commitment and calculative commitment) (Gustafsson,2005). In this research, customer satisfaction is the dependent variable for customer retention. Moreover, a thorough understanding on the automated service attributes – customization, convenience and reliability) can help in developing customer satisfaction and retention. Basing on past literature reviews, customer satisfaction can be considered as the key performance for retention as compared to other customer retention predictions. From previous evidences of the literature review, researchers had evaluated a broader view of customer retention towards public railway transportation. Two stages of the framework have guided the researchers to study an extensive research on service automation. Generally, the first stage mainly focuses on automation attributes and the second part is on how the automation services also lead to customer retention. These two stages of framework signify that the study is done to evaluate two different theories (customer satisfaction and customer retention).

Research Implications The results gathered provide valuable input for marketing managers of transportation companies specifically and service organizations in general to improve service levels. Berry et al (1990) stated that managers could work toward improving problem-resolution as a way to challenge customer perceptions, particularly those that the company is not aware of. Additionally, there are opportunities to build strong loyalty among consumers, particularly working adults and young professionals, by examining and evaluating their levels of satisfaction and retention relating to their preference towards automation in terms of scale and scope that can be made available in the railway transportation service.

Furthermore, this study might help marketers to design promotions that not only specifically target this group of rail commuters but also target car owners who have not considered rail transportation as a genuine alternative to intra-city and inter-city commuting. This can potentially open up a bigger market that has a corollary effect of reducing traffic congestion and more importantly, from the point of view of the travelling public – not just to help reduce the worsening traffic congestion in the cities, but also to significantly reduce the amount of travelling time. From the results, it can be seen that a majority of the respondents respond positively toward using rail transport – particularly because of the automation services provided in railway transportation in Malaysia. Based on this,



marketers can use this insight by coming up with strategic and tactical communication plans to attract and influence more Malaysians to use rail transport.

Conclusion In conclusion, the factor of reliability presents an insignificant relationship in influencing customer satisfaction on service encounters. However this does not mean the authorities should neglect the reliability in the use of technology because it is likely that consumers expect it rather than demanding it. [Note: This is an area of further research]. After all, technology is here to stay. It is the goal of service organizations everywhere to continually work toward facilitating service encounters which will enhance the service delivery process and increase users‟ satisfaction through the adoption of technology in this specific industry. Lastly, this study concludes that customer satisfaction in service encounters is a prerequisite to customer retention – especially when automation is progressively being introduced to make it easy and seamless for commuters at large. Furthermore, it debunks the old stereotype notion that railway transport is inefficient and outdated – instead it has made the quantum leap into the 21st century.

Limitations of Study Due to the lack of secondary sources on service automation in rail transportation I Malaysia, there were insufficient evidences to support the direct relationships of the attributes of automated services and customer satisfaction. Hence, the proposed framework was employed in this research because only a very limited study had been done in examining the role of customer satisfaction in service encounters with regard to the process of automation in rail transportation. One specific area which we could have focused more is the relationship between convenience and customer satisfaction. We didn‟t attach much importance to it as much as the other factors – e.g. in the way we designed our questionnaire – that we take „convenience‟ very much for granted. This research only examines the attitudes of 120 respondents – and this limits our ability to accurately generalise our findings in a manner that can represent the Malaysian population. The sample was just too small.

Recommendations on Future Research Since quantitative method was used in the current research by addressing the proposed objectives in two stages. Firstly, it is to evaluate the relationship between the automation service attribution leads to customer satisfaction, whereas the second stage is evaluating the customer satisfaction and customer retention. The researchers suggest that future research adopt both qualitative and quantitative method in conducting similar research topics in order to gain a deeper understanding as well as further reinforcing our results.



Moreover, as this research has its own limitations investigating the service automation that influences customer satisfaction and retention – future research could expand this study to include a bigger sample from different urban epicenters within the metropolitan Klang Valley. Since the current study is targeted towards customer satisfaction and retention, further research can be done by studying other approaches involving customer expectations, customer intentions and customer preferences in this area of service encounters to enrich our understanding of the issues facing the public transportation industry in Malaysia.

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Appendix Questionnaire

Dear Respondents, We are students from SUNWAY UNIVERSITY who currently enroll in Degree in Business Studies. We are conducting a survey on “The impact of service automation on service encounters satisfaction and customer retention in rail transportation”. The service encounter satisfaction and customer retention questionnaires are distributed to determine the customer satisfaction level and their willingness to stay with the automated services. With advancement of technology, the introduction of automated system is critical for the service provider to enhance the service encounter process and to satisfy individual’s travel demands. The examples of automated service are electronic arrival system, fare collection machine, ticketing machine, CCTV security system and etc. We would like to include your valuable opinions in this research to provide us important information. Please help us by taking a few minutes to complete this questionnaire. Thank You.

Section A

1. How frequent you take rail transportation?

Less than 1 time per week 1-3 times per week More

than 3 times per week

2. Are you aware of the automated services provided at the station?

Yes No

3. Have you been using automated services when travelling with rail

transportation?

Yes No

Section B Please rate the following statement. (1-strongly agree, 2-agree, 3-nuetral, 4-disagree, 5-strongly disagree) **Note: Automated service refers to electronic arrival system, fare collection machine, electronic ticketing machine, Touch & Go system and CCTV security system.

Automated Service Attributes 1 2 3 4 5

1. The automated service delivery is consistent.

2. The automated service is accessible.

3. The automated service functions.

4. The automated service is dependable.

5. The automated service is accurate.

6. The automated service provides me different

range of services.



Section C Please rate the following statement. (1-strongly agree, 2-agree, 3-nuetral, 4-disagree, 5-strongly disagree)

Section D Please rate the following statement.

7. The automated service is conveniently

located.

8. The automated service is compatible in other

railway platforms (e.g. Touch & Go card).

9. The automated service is a convenient way to

conduct service transactions.

10. The automated service is less time

consuming.

11. The automated service offers me variety of services that satisfy me specific needs.

12. The automated service offers me better

accessibility in travelling.

Service Encounter Satisfaction 1 2 3 4 5

13. I am satisfied with the overall performance of

existing automated service in rail

transportation.

14. I am satisfied with the speed of automated service delivery.

15. The existing automated service exceeds my

expectations.

16. I have truly enjoyed the process of using

automated services in transportation.

17. The automated service in railway

transportation made the customer experience

more satisfying.

18. I prefer automated services compared with

face-to-face customer service.

Customer Retention 1 2 3 4 5

19. I will choose automated services again when

travelling with railway transportation.

20. I will recommend my friends and family to use

automated services in railway transportation.

21. I will always consider automated service as



(1-strongly agree, 2-agree, 3-nuetral, 4-disagree, 5-strongly disagree)

Section E: Socio-Demographics

1. Gender: 口 Female 口 Male

2. Age: 口 >17 18 - 24 口 25 - 34 口 35 – 44 口 > 45

3. Marital Status: 口 Single 口 Married 口 Others

4. Employment Status: 口 Employed 口 Students 口 Housewife

口 Retired 口 Other

5. Educational Level: 口 Secondary education 口 Degree 口Master

口 Professional

my first choice.

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